Configuration

This page displays the parameters configured in the config.json file for calculating nutrient targets, identifying user medical information, and data mining for daily meal plans. To modify the parameters, please update the config.json file.

MET and EER Formula

This formula is for MET (Metabolic Equivalent of Task) and EER(Estimated Energy Requirement) calculation.

Name	Formula
MET	1 if level == 1 else 1.11 if gender == '1' and level == 2 else 1.25 if gender == '1' and level == 3 else 1.48 if gender == '1' and level == 4 else 1.48 if gender == '1' else 1.12 if gender == '2' and level == 2 else 1.27 if gender == '2' and level == 3 else 1.45 if gender == '2' and level == 4 else 1.45
EER	662 + -9.53 * age + met( 15.91 weight + 5.396 * height) if gender == '1' else 354 + -6.91 * age + met( 9.36 weight + 7.26 * height)

Nutrition Target Calculation Logic

This table is for nutrition target calculation.

Nutrition	Conditions	Formula
calorie	default	EER
fiber	opioid_misuse low_density_lipoprotein user_high_fiber default	38 if gender == '1' and age <= 50 else 25 if gender == '2' and age <= 50 else 31 if gender == '1' else 21 38 if gender == '1' and age <= 50 else 25 if gender == '2' and age <= 50 else 31 if gender == '1' else 21 38 if gender == '1' and age <= 50 else 25 if gender == '2' and age <= 50 else 31 if gender == '1' else 21 INFINITY
carb	diabetes user_low_carb default	EER * 0.65 / 4.0 EER * 0.65 / 4.0 EER * 0.45 / 4.0
protein	default	math.ceil(weight * 0.8)
saturated_fat	default	math.ceil(EER / 2000.0 * 13.0)
sugar	over_weight diabetes opioid_misuse user_low_sugar default	36 if gender == '1' else 25 36 if gender == '1' else 25 36 if gender == '1' else 25 36 if gender == '1' else 25 50
cholesterol	low_density_lipoprotein user_low_cholesterol default	200 200 300
sodium	blood_pressure diabetes opioid_misuse user_low_sodium default	1500 1500 1500 1500 2300
phosphorus	user_low_phosphorus default	700 4000 if gender == '1' else 3000
potassium	blood_pressure default	2600 if gender == '2' else 3400 5000
iron	anemia default	18 if gender == '2' else 8 45
calcium	osteoporosis default	1200 if gender == '2' else 1000 2500 if gender == '1' else 2000
folic_acid	anemia default	400 1000
vitamin_c	default	2000
vitamin_d	osteoporosis default	20 if gender == '2' else 15 100
vitamin_b12	anemia default	0 2.4

Nutrition Impact by Conditions

This section represents nutrition impacts on medical condition or user preference.

health

No specific nutrition.

under_weight

No specific nutrition.

over_weight

sugar

blood_pressure

sodium
potassium

low_density_lipoprotein

fiber

blood_urea_nitrogen

No specific nutrition.

diabetes

sugar
sodium

anemia

iron
vitamin_c
folic_acid
vitamin_b12

osteoporosis

calcium
vitamin_d
vitamin_c

opioid_misuse

fiber
sugar
sodium

user_low_phosphorus

phosphorus

user_low_saturated_fat

No specific nutrition.

user_low_carb

No specific nutrition.

user_low_calorie

No specific nutrition.

user_low_cholesterol

No specific nutrition.

user_low_sugar

sugar

user_high_calorie

No specific nutrition.

user_low_protein

No specific nutrition.

user_low_sodium

sodium

user_high_potassium

potassium

user_high_protein

No specific nutrition.

user_high_fiber

fiber

user_high_folate_acid

folic_acid

user_high_iron

iron

user_high_vitamin_b12

vitamin_b12

user_high_calcium

calcium

user_high_vitamin_c

vitamin_c

user_high_vitamin_d

vitamin_d

Nutrition Target Range Setting Logic

This table represents nutrition target range setting logics.

Nutrition	Conditions	Formula
calorie	over_weight under_weight user_low_calorie default	[0, math.ceil(EER * 1.1 / 100) * 100] [math.floor(calc_EER(gender, age, height, weight, 1) * 0.9 / 100) * 100, math.ceil(EER * 1.1 / 100) * 100] [0, math.ceil(EER * 1.1 / 100) * 100] [math.floor(calc_EER(gender, age, height, weight, 1) * 0.9 / 100) * 100, math.ceil(EER * 1.1 / 100) * 100]
fiber	opioid_misuse low_density_lipoprotein user_high_fiber default	[38, 99999] if gender == '1' and age <= 50 else [25, 99999] if gender == '2' and age <= 50 else [31, 99999] if gender == '1' else [21, 99999] [38, 99999] if gender == '1' and age <= 50 else [25, 99999] if gender == '2' and age <= 50 else [31, 99999] if gender == '1' else [21, 99999] [38, 99999] if gender == '1' and age <= 50 else [25, 99999] if gender == '2' and age <= 50 else [31, 99999] if gender == '1' else [21, 99999] [0, INFINITY]
carb	diabetes user_low_carb default	[0, math.ceil((EER * 0.65 / 4.0) / 50) * 50] [0, math.ceil((EER * 0.65 / 4.0) / 50) * 50] [math.floor((EER * 0.45 / 4.0) / 50) * 50, math.ceil((EER * 0.65 / 4.0) / 50) * 50]
protein	blood_urea_nitrogen opioid_misuse user_low_protein user_high_protein default	[0, math.ceil((weight * 0.8) / 10) * 10] [math.ceil((weight * 0.8) / 10) * 10, INFINITY] [0, math.ceil((weight * 0.8) / 10) * 10] [math.ceil((weight * 0.8) / 10) * 10, INFINITY] [0, math.ceil((weight * 0.8) / 10) * 10]
saturated_fat	over_weight user_low_saturated_fat default	[0,math.ceil((EER / 2000.0 * 13.0) / 5) * 5] [0,math.ceil((EER / 2000.0 * 13.0) / 5) * 5] [0, INFINITY]
sugar	over_weight diabetes opioid_misuse user_low_sugar default	[0, 36 if gender == '1' else 25] [0, 36 if gender == '1' else 25] [0, 36 if gender == '1' else 25] [0, 36 if gender == '1' else 25] [36 if gender == '1' else 25, 50]
cholesterol	low_density_lipoprotein user_low_cholesterol default	[0, 200] [0, 200] [0, 300]
sodium	blood_pressure diabetes opioid_misuse user_low_sodium default	[0, 1500] [0, 1500] [0, 1500] [0, 1500] [1500, 2300]
phosphorus	user_low_phosphorus default	[0, 700] [700, 4000 if gender == '1' else 3000]
potassium	blood_pressure default	[2600 if gender == '2' else 3400, 5000] [0, 5000]
iron	anemia default	[18 if gender == '2' else 8, 45] [0, 45]
calcium	osteoporosis default	[1200 if gender == '2' else 1000, 2500 if gender == '1' else 2000] [0, 2500 if gender == '1' else 2000]
folic_acid	anemia default	[400, 1000] [0, 1000]
vitamin_c	anemia osteoporosis user_high_vitamin_c default	[90 if gender == '1' else 75, 2000] [90 if gender == '1' else 75, 2000] [90 if gender == '1' else 75, 2000] [0, 2000]
vitamin_d	osteoporosis default	[20 if gender == '2' else 15, 100] [0, 100]
vitamin_b12	anemia default	[0, 2.4] [0, 2.4]

Medical Conditions and Nutrition tags

This table represents medical condition setting logic and nutrition tags based on medical conditions.

Condition	Condition Details	Tags
over_weight	BMI>=25 and (WAIST >=102 if gender ==1 else WAIST>=88)	over_weight low_calorie
under_weight	BMI<18.5	under_weight high_calorie
blood_pressure	systolic >= 130 or diastolic >= 80	low_sodium high_potassium blood_pressure
low_density_lipoprotein	ldl and ldl>3.3	low_cholesterol high_fiber low_saturated_fat low_density_lipoprotein
blood_urea_nitrogen	blood_urea_nitrogen>=7.1	low_protein blood_urea_nitrogen
opioid_misuse	Active User: (heroin.isin([4,-1])==False) \| (meth.isin([4,-1])==False)\| (cocaine.isin([4,-1])==False) Multum Drug Therapeutic Category: level_one==57 and level_two==58 and (level_three==60 or level_three==191) Opioid Used: (drunk_days > 90) and ( (used==1 and drug_id in multum_drug_therapeutic_categories ) or (icd_code=='F11.2' or icd_code=='F11.23') ) Condition: user in active_user or user in opioid_used	low_sugar high_protein high_fiber opioid_misuse
diabetes	glucose >= 7.0 and glycohemoglobin >= 6.5	low_sugar high_fiber diabetes
anemia	red_blood_cell_count<= 4 and (hemoglobin<13.2 if gender ==1 else hemoglobin<11.6)	high_iron high_vitamin_c high_folate_acid high_vitamin_b12 anemia
osteoporosis	osteoporosis==1	high_calcium high_vitamin_d high_vitamin_c osteoporosis